Federated learning (FL) is a privacy-preserving framework for distributed learning that enables edge devices to address a learning task, e.g., training a global model, jointly without need for sharing their local data [1]}, [2]}, [3]}. This ensures device privacy and provides a joint scheme for learning that performs considerably better than individual distributed learning while keeping the communication load of the system tractable [4]}, [5]}, [6]}.
